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Abstract ENSO and the mean zonal sea surface temperature gradient (dSST) of the tropical Pacific are important drivers of global climate and vary on decadal to centennial time scales. However, the relationship between dSST and ENSO cannot be assessed with the short instrumental record, and is uncertain in proxy data, with intervals of both stronger and weaker ENSO postulated to occur with overall strong dSST in the past. Here we assess the ENSO–dSST relationship during the last millennium using general circulation models (GCMs) participating in phase 3 of the Paleoclimate Modeling Intercomparison Project. Last millennium GCM simulations show diversity in the strength and direction of the ENSO–dSST relationship. Yet, the models that best simulate modern tropical Pacific climate frequently have a more negative ENSO–dSST correlation. Thus, last millennium tropical Pacific climate simulations support the likelihood of enhanced ENSO during decadal to centennial periods of reduced tropical Pacific dSST. However, the alternating directional ENSO–dSST relationship in all model simulations suggests that this relationship is not constant through time and is likely controlled by multiple mechanisms.
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Fingerprinting Low-Frequency Last Millennium Temperature Variability in Forced and Unforced Climate Models
Abstract Constraining unforced and forced climate variability impacts interpretations of past climate variations and predictions of future warming. However, comparing general circulation models (GCMs) and last millennium Holocene hydroclimate proxies reveals significant mismatches between simulated and reconstructed low-frequency variability at multidecadal and longer time scales. This mismatch suggests that existing simulations underestimate either external or internal drivers of climate variability. In addition, large differences arise across GCMs in both the magnitude and spatial pattern of low-frequency climate variability. Dynamical understanding of forced and unforced variability is expected to contribute to improved interpretations of paleoclimate variability. To that end, we develop a framework for fingerprinting spatiotemporal patterns of temperature variability in forced and unforced simulations. This framework relies on two frequency-dependent metrics: 1) degrees of freedom (≡N) and 2) spatial coherence. First, we useNand spatial coherence to characterize variability across a suite of both preindustrial control (unforced) and last-millennium (forced) GCM simulations. Overall, we find that, at low frequencies and when forcings are added, regional independence in the climate system decreases, reflected in fewerNand higher coherence between local and global mean surface temperature. We then present a simple three-box moist-static-energy-balance model for temperature variability, which is able to emulate key frequency-dependent behavior in the GCMs. This suggests that temperature variability in the GCM ensemble can be understood through Earth’s energy budget and downgradient energy transport, and allows us to identify sources of polar-amplified variability. Finally, we discuss insights the three-box model can provide into model-to-model GCM differences. Significance StatementForced and unforced temperature variability are poorly constrained and understood, particularly that at time scales longer than a decade. Here, we identify key differences in the time scale–dependent behavior of forced and unforced temperature variability using a combination of numerical climate models and principles of downgradient energy transport. This work, and the spatiotemporal characterizations of forced and unforced temperature variability that we generate, will aid in interpretations of proxy-based paleoclimate reconstructions and improve mechanistic understanding of variability.
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- PAR ID:
- 10462793
- Publisher / Repository:
- American Meteorological Society
- Date Published:
- Journal Name:
- Journal of Climate
- Volume:
- 36
- Issue:
- 20
- ISSN:
- 0894-8755
- Format(s):
- Medium: X Size: p. 7005-7023
- Size(s):
- p. 7005-7023
- Sponsoring Org:
- National Science Foundation
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